Re: Cellular Automata vs. Agent-Based Models

[Marcus G. Daniels]

>>>>> "DT" == Dawn Trembath <address@hidden> writes:

DT> I'm now trying to understand the practical
DT> issue of why one would choose to use a CA representation, vs an
DT> ABM approach.

One thing Nelson didn't mention is that Swarm was designed to
facilitate fine-grained discrete event simulation.  This is where the
actions that an agent performs have minimal mechanism and
iterative behaviors happen via the scheduling machinery.¹

Maximizing the information exposed to the scheduling machinery and
minimizing the amount of mechanism hidden in ad-hoc code brings an
agent-based model fairly close to CAs in terms of something you can
grab on to for statistical or analytical purposes.  Essentially, an
exact time-indexed dataset with tags that reflect understandable
transformations of (typically local) state.

On the other hand, a basic feature of ABM is that you can engineer
agents to do things.  You may actually know (or have a rich intuition
about) how some of the components in a model actually work.  If you
want to use this kind of information for modeling, and this
information is complex or doesn't especially fit into a clean
theoretical framework, programming is a practical and precise way to
express it.

Although the traditional use for Swarm is to synthesize models, the
design also avoids obstructing the deconstruction of models.

¹ As the event distribution of a simulation gets more dense, having an
explicit representation of concurrent events also makes it possible to
amortize the weight of the extra scheduling abstraction (messages to
`Action' objects instead of pure compiled code) by distributing what
is logically concurrent to different physical CPUs.






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